TY  - INPR
A1  - Kennedy, Eamonn
A1  - Vadlamani, Shashank
A1  - Lindsey, Hannah M.
A1  - Lei, Pui-Wa
A1  - Jo-Pugh, Mary
A1  - Adamson, Maheen
A1  - Alda, Martin
A1  - Alonso-Lana, Silvia
A1  - Ambrogi, Sonia
A1  - Anderson, Tim J.
A1  - Arango, Celso
A1  - Asarnow, Robert F.
A1  - Avram, Mihai
A1  - Ayesa-Arriola, Rosa
A1  - Babikian, Talin
A1  - Banaj, Nerisa
A1  - Bird, Laura J.
A1  - Borgwardt, Stefan
A1  - Brodtmann, Amy
A1  - Brosch, Katharina
A1  - Caeyenberghs, Karen
A1  - Calhoun, Vince D.
A1  - Chiaravalloti, Nancy D.
A1  - Cifu, David X.
A1  - Crespo-Facorro, Benedicto
A1  - Dalrymple-Alford, John C.
A1  - Dams-O’Connor, Kristen
A1  - Dannlowski, Udo
A1  - Darby, David
A1  - Davenport, Nicholas
A1  - DeLuca, John
A1  - Díaz-Caneja, Covadonga M.
A1  - Disner, Seth G.
A1  - Dobryakova, Ekaterina
A1  - Ehrlich, Stefan
A1  - Esopenko, Carrie
A1  - Ferrarelli, Fabio
A1  - Frank, Lea E.
A1  - Franz, Carol
A1  - Fuentes-Claramonte, Paola
A1  - Genova, Helen
A1  - Giza, Christopher C.
A1  - Goltermann, Janik
A1  - Grotegerd, Dominik
A1  - Gruber, Marius
A1  - Gutiérrez-Zotes, Alfonso
A1  - Ha, Minji
A1  - Haavik, Jan
A1  - Hinkin, Charles
A1  - Hoskinson, Kristen R.
A1  - Hubl, Daniela
A1  - Irimia, Andrei
A1  - Jansen, Andreas
A1  - Kaess, Michael
A1  - Kang, Xiaojian
A1  - Kenney, Kimbra
A1  - Keřková, Barbora
A1  - Khlif, Mohamed Salah
A1  - Kim, Minah
A1  - Kindler, Jochen
A1  - Kircher, Tilo
A1  - Knı́žková, Karolina
A1  - Kolskår, Knut K.
A1  - Krch, Denise
A1  - Kremen, William S.
A1  - Kuhn, Taylor
A1  - Kumari, Veena
A1  - Kwon, Jun Soo
A1  - Langella, Roberto
A1  - Laskowitz, Sarah
A1  - Lee, Jungha
A1  - Lengenfelder, Jean
A1  - Liebel, Spencer W.
A1  - Liou-Johnson, Victoria
A1  - Lippa, Sara M.
A1  - Løvstad, Marianne
A1  - Lundervold, Astri
A1  - Marotta, Cassandra
A1  - Marquardt, Craig A.
A1  - Mattos, Paulo
A1  - Mayeli, Ahmad
A1  - McDonald, Carrie R.
A1  - Meinert, Susanne
A1  - Melzer, Tracy R.
A1  - Merchán Naranjo, Jessica
A1  - Michel, Chantal
A1  - Morey, Rajendra A.
A1  - Mwangi, Benson
A1  - Myall, Daniel J.
A1  - Nenadić, Igor
A1  - Newsome, Mary R.
A1  - Nunes, Abraham
A1  - O’Brien, Terence J.
A1  - Oertel, Viola
A1  - Ollinger, John
A1  - Olsen, Alexander
A1  - Ortiz Garcı́a de la Foz, Victor
A1  - Ozmen, Mustafa
A1  - Pardoe, Heath
A1  - Parent, Marise
A1  - Piras, Fabrizio
A1  - Piras, Federica
A1  - Pomarol-Clotet, Edith
A1  - Repple, Jonathan
A1  - Richard, Geneviève
A1  - Rodriguez, Jonathan
A1  - Rodriguez, Mabel
A1  - Rootes-Murdy, Kelly
A1  - Rowland, Jared
A1  - Ryan, Nicholas P.
A1  - Salvador, Raymond
A1  - Sanders, Anne-Marthe
A1  - Schmidt, André
A1  - Soares, Jair C.
A1  - Spalleta, Gianfranco
A1  - Španiel, Filip
A1  - Stasenko, Alena
A1  - Stein, Frederike
A1  - Straube, Benjamin
A1  - Thames, April
A1  - Thomas-Odenthal, Florian
A1  - Thomopoulos, Sophia I.
A1  - Tone, Erin
A1  - Torres, Ivan
A1  - Troyanskaya, Maya
A1  - Turner, Jessica A.
A1  - Ulrichsen, Kristine M.
A1  - Umpierrez, Guillermo
A1  - Vilella, Elisabet
A1  - Vivash, Lucy
A1  - Walker, William C.
A1  - Werden, Emilio
A1  - Westlye, Lars T.
A1  - Wild, Krista
A1  - Wroblewski, Adrian
A1  - Wu, Mon-Ju
A1  - Wylie, Glenn R.
A1  - Yatham, Lakshmi N.
A1  - Zunta-Soares, Giovana B.
A1  - Thompson, Paul M.
A1  - Tate, David F.
A1  - Hillary, Frank G.
A1  - Dennis, Emily L.
A1  - Wilde, Elisabeth A.
T1  - Bridging big data: procedures for combining non-equivalent cognitive measures from the ENIGMA Consortium
T2  - bioRxiv
N2  - Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/73160
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-731606
IS  - 2023.01.16.524331 Version 1
ER  -